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OLS Field Name OLS Field Data
Main Title Predictive modeling with SAS Enterprise Miner : practical solutions for business applications /
Author Sarma, Kattamuri S.
Publisher SAS Institute,
Year Published 2013
OCLC Number 872652449
ISBN 9781607647676; 1607647672
Subjects Data mining. ; Regression analysis--Computer programs. ; Statistics--Data processing.
Holdings
Library Call Number Additional Info Location Last
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Status
ELBM  QA76.9.S27 2013 AWBERC Library/Cincinnati,OH 09/27/2016
Edition 2nd ed.
Collation xxii, 478 pages : illustrations ; 28 cm.
Notes Includes bibliographical references and index.
Contents Notes Research strategy -- Getting started with predictive modeling -- Variable selection and transformation of variables -- Building decision tree models to predict response and risk -- Neural network models to predict response and risk -- Regression models -- Comparison and combination of different models -- Customer profitability -- Introduction to predictive modeling with textual data. Learn the theory behind and methods for predictive modeling using SAS Enterprise Miner Learn how to produce predictive models and prepare presentation-quality graphics in record time with Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications, Second Edition. If you are a graduate student, researcher, or statistician interested in predictive modeling; a data mining expert who wants to learn SAS Enterprise Miner; or a business analyst looking for an introduction to predictive modeling using SAS Enterprise Miner, you'll be able to develop predictive models quickly and effectively using the theory and examples presented in this book. Author Kattamuri Sarma offers the theory behind, programming steps for, and examples of predictive modeling with SAS Enterprise Miner, along with exercises at the end of each chapter. You'll gain a comprehensive awareness of how to find solutions for your business needs. This second edition features expanded coverage of the SAS Enterprise Miner nodes, now including File Import, Time Series, Variable Clustering, Cluster, Interactive Binning, Principal Components, AutoNeural, DMNeural, Dmine Regression, Gradient Boosting, Ensemble, and Text Mining. Develop predictive models quickly, learn how to test numerous models and compare the results, gain an in-depth understanding of predictive models and multivariate methods, and discover how to do in-depth analysis. Do it all with Predictive Modeling with SAS Enterprise Miner.
Place Published Cary, NC
Corporate Au Added Ent SAS Institute.
PUB Date Free Form 2013
BIB Level m
Medium unmediated
Content text
Carrier volume
Cataloging Source OCLC/T
Merged OCLC records 881399440
OCLC Time Stamp 20160923060111
Language eng
Origin OCLC
Type CAT
OCLC Rec Leader 03460cam 2200457Ka 45010